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Jay Smith's PhD Thesis Abstract

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Robust Resource Allocation in Heterogeneous Parallel and Distributed Computing Systems

Ph.D., Colorado State University, Aug. 2008

Co-Major Professors: H. J. Siegel and Anthony A. Maciejewski

In a heterogeneous distributed computing environment, it is often advantageous to allocate
system resources in a manner that optimizes a given system performance measure. However,
this optimization is often dependent on system parameters whose values are subject to
uncertainty. Thus, an important research problem arises when system resources must be
allocated given uncertainty in system parameters. Robustness can be defined as the degree
to which a system can function correctly in the presence of parameter values different from
those assumed. In this research, we define mathematical models of robustness in both static
and dynamic stochastic environments. In addition, we model dynamic environments where
estimates of system parameter values are provided as point estimates where these estimates
are known to deviate substantially from their actual values.

The main contributions of this research are (1) mathematical models of robustness
suitable for dynamic environments based on single estimates of system parameters (2) a
mathematical model of robustness applicable to environments where the uncertainty in
system parameters can be modeled stochastically, (3) a demonstration of the use of this
metric to design resource allocation heuristics in a static environment, (4) a mathematical
model of robustness in a stochastic dynamic environment, (5) we demonstrate the utility
of this dynamic robustness metric through the design of resource allocation heuristics, (6)
the derivation of a robustness metric for evaluating resource allocation decisions in an
overlay network along with a near optimal resource allocation technique suitable to this
environment.